Build a speech-to-speech translation that automatically detects the spoken language and runs locally on Android, iOS, Mac, Windows, Linux, embedded and web. No cloud, no latency.
On-device speech-to-speech translation combines Bat Spoken Language Identification, Cheetah Streaming Speech-to-Text, Zebra Translate, and Orca Streaming Text-to-Speech in a single local pipeline. Most implementations require the user to select a source language upfront or still route at least one stage through a cloud API. This pipeline eliminates both constraints: language is detected automatically, and every stage runs on the device.
Bat identifies the spoken language automatically from the audio stream and routes it to the correct recognition model with no user input required. A French speaker and a German speaker can both address the same device without any configuration change. Runs on-device with no audio uploaded.
Cheetah Streaming Speech-to-Text beats Google Cloud STT in word error rate and word emission latency across all tested languages, and outperforms Azure STT in several benchmarks, per the open-source real-time transcription benchmark — even before it's customized for the use case. It emits words at 590 ms median latency, typically one word behind the speaker. Cheetah requires less compute than any other local engine tested. Result: no tradeoff on accuracy, latency, or privacy, and no minimum hardware requirements.
Zebra, Picovoice's on-device translation SDK, returns up to 120 words per second — far faster than the average 2 words per second a person speaks or the 5 words per second a person reads. The speed does not come at the cost of accuracy: it matches the accuracy of Opus, one of the best-known open translation models. On-device processing also gives Zebra a competitive edge: zero network latency, which can never be achieved by cloud translation APIs such as Google Translate and DeepL.
Most high-quality TTS solutions require hundreds of megabytes of RAM. Orca TTS uses 29 MB peak memory, 10–50× less than any other on-device alternative, except for ESpeak. This makes Orca the only natural-sounding TTS deployable in any environment, including browser tabs, mobile apps with strict out-of-memory limits, and embedded devices.
Officers conducting interviews, taking statements, and communicating with witnesses or suspects who speak different languages in the field have no time to wait for a human interpreter. Running a voice translator on-device matters for two reasons: body cameras and patrol devices frequently operate in areas with poor connectivity, and audio of police interactions cannot be routed through a third-party cloud service.
Paramedics, firefighters, and emergency dispatchers face language barriers in situations where seconds matter. Someone in stress who cannot describe symptoms, or a bystander who cannot explain what happened, can delay critical decisions. On-device translation works even when connectivity is poor, adds no perceptible latency, and keeps sensitive audio and personally identifiable info off external servers.
Tourists and expats navigate markets, hotels, restaurants, and transport in countries where they do not speak the language. The on-device advantage: translation works on a plane, underground, in rural areas, and anywhere roaming data is expensive or unavailable. No cloud dependency means no degraded experience when the signal drops mid-conversation, regardless of where they are.
Border agents interview travelers who may or may not know English or the local language in noisy, time-pressured environments. The audio is sensitive and cannot be sent to a commercial cloud API. Connectivity at land border crossings and remote processing centers is often unreliable. On-device speech translation addresses these challenges by keeping audio off any external server and eliminating network latency.
Conferences, summits, trade shows, and sporting events with multilingual attendees need real-time translation that does not depend on venue wifi. Convention center networks are notoriously overloaded during large events. On-device translation runs on the attendee's own device or the event hardware with no dependency on the local network, no per-attendee cloud cost, and no audio passing through an event organizer's infrastructure.
Agents may need to handle calls from customers who speak different languages or just identify the language to route to the right person. Agents can follow any conversation in real time and respond through synthesized speech in the caller's language. On-device voice translators eliminate the latency introduced by cloud translation APIs on every utterance, with no audio sent to a third-party service.
A complete working recipe in Python. Open-source on GitHub. Runs 100% on-device.